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relationalai.experimental.solvers.exp()

exp(arg: dsl.Instance|SolverExpression) -> SolverExpression

Returns a solver expression that represents raised to the power of the input arg, where is Euler’s number (approximately 2.71828). Must be called in a Model.rule() or solvers.operators() context.

Parameters

Name	Type	Description
`arg`	`dsl.Instance`, `SolverExpression`	A numeric solver variable or expression to exponentiate.

Returns

A SolverExpression object.

Example

Use exp() with the SolverModel.constraint() method to model non-linear growth or penalty effects:

import relationalai as rai
from relationalai.experimental import solvers

# Create a model.
model = rai.Model("DietaryConstraints")

# Declare entity types.
Food = model.Type("Food")
Day = model.Type("Day")
Portion = model.Type("Portion")

23 collapsed lines
# Declare properties.
Food.calories.declare()
Food.fat.declare()
Food.salt.declare()
Food.carbs.declare()
Portion.food.declare()
Portion.day.declare()

# Define foods.
with model.rule():
    Food.add(name="Oatmeal").set(calories=150, fat=3, salt=0.1, carbs=27)
    Food.add(name="Egg").set(calories=70, fat=5, salt=0.2, carbs=1)
    Food.add(name="Banana").set(calories=100, fat=0.5, salt=0.0, carbs=26)
    Food.add(name="Toast").set(calories=80, fat=1, salt=0.3, carbs=15)

# Define days.
with model.rule():
    for name in ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]:
        Day.add(name=name)

# Define all possible food portions for each day.
with model.rule():
    Portion.add(food=Food(), day=Day())

# Create a solver model.
solver_model = solvers.SolverModel(model)

# Define solver variables for portions.
with model.rule():
    portion = Portion()
    solver_model.variable(
        portion,
        name_args=["portion", portion.food.name, portion.day.name],
        lower=0,
        upper=5,
    )

# Constrain exponential fat total to discourage high-fat meals.
with solvers.operators():
    portion = Portion()
    total_fat = solvers.sum(portion.food.fat * portion, per=[portion.day])
    solver_model.constraint(solvers.exp(total_fat) <= 500)

See Also